The invention relates generally to image reconstruction and more particularly to techniques for reconstructing cone-beam projection data, with reduced cone-beam artifacts.
Most modern Computed Tomography (CT) scanners are based on a third generation architecture, which embodies a single x-ray source and a large x-ray detector. The x-ray detector can be a one-dimensional, usually curved, array of detector cells, resulting in fan-beam geometry. In axial scans (i.e. the patient table does not move during the gantry rotation) the result is a purely planar dataset to which two-dimensional (“2D”) filtered backprojection (FBP) can be applied. Reconstruction is theoretically exact, and any possible image artifacts may come from physical limitations of the scanner, such as quantum noise, aliasing, beam hardening, and scattered radiation.
Since about 1990, multi-slice or multi-detector-row CT systems have become the standard CT architecture for premium medical scanners, wherein the detector has multiple rows, i.e. a two-dimensional array of detector cells, resulting in cone-beam geometry. Since these geometries do not result in planar datasets, 2D image reconstruction algorithms will not be based on the correct scan geometry and may result in cone-beam artifacts. For the axial scan mode, Feldkamp, Davis, and Kress proposed a three-dimensional (“3D”) cone-beam reconstruction algorithm (“FDK algorithm”) that adapts 2D fan-beam filtered backprojection (FBP) to cone-beam geometry. The FDK algorithm works well near the mid-plane and near the center of rotation, but artifacts occur and get worse as the cone-angle increases. For 40 mm-coverage scanners (which typically corresponds to about a 4 degree cone-angle) significant artifacts occur, particularly towards the z=−20 mm and z=20 mm slices. The raw CT data is actually fundamentally incomplete in 3D axial scans, and therefore, even the best thinkable algorithm will result in artifacts in some cases.
On the other hand, in helical cone-beam scans, the data is fundamentally complete (provided the table speed is not too high compared to the gantry rotation speed and the slice thickness) and therefore exact reconstruction is possible. The FDK algorithm has been adapted for helical scan modes, but results in non-exact or approximate reconstruction. Accordingly, exact 3D helical cone-beam reconstruction algorithms (including the Katsevitch algorithm) have been developed, which perform filtering operations along special filter lines followed by backprojection.
Exact reconstruction techniques, while enabling the accurate reconstruction of a three-dimensional image from two-dimensional projection data with reduced cone-beam artifacts, are generally applicable only to specific types of source trajectories such as, for example, helix, saddles, variable pitch helix and circle plus arc trajectories. However, although exact reconstruction techniques exist for many trajectories including the helix, approximate algorithms are often used in practice since these algorithms provide advantages such as reduced noise, improved noise uniformity, improved spatial resolution, computational efficiency, and/or resistance to motion artifacts. In addition, for circular scan or circular segment trajectories, where certain well known criteria, such as, for example, Tuy's data completeness condition is not satisfied everywhere in the imaging volume, the data acquired along these trajectories used to reconstruct the image data, often results in cone-beam artifacts. In addition, these cone-beam artifacts are typically much more severe for reconstructions that use less than a full scan of data, not necessarily only due to the fact that data is missing, but even because of the fact that the available data is mishandled.
It would be desirable to develop techniques for reconstructing cone-beam projection data acquired along circular scan trajectories and helical trajectories, with reduced cone-beam artifacts. In addition, it would be desirable to develop techniques for reconstructing cone-beam projection data acquired along circular scan trajectories and helical trajectories, while still achieving desired image quality characteristics, such as, high computational efficiency, good dose usage and resistance to motion artifacts.